Methods and systems for generating one or more emoticons for one or more users

ABSTRACT

A method for generating one or more emoticons for one or more users with respect to one or more fictional characters is provided. The method includes receiving a first image generated by a multiple localized discriminator (MLD) generative adversarial network (GAN) based on a set of features from multiple sets of features associated with the one or more fictional characters, resulting in generation of an output value associated with each of the plurality of discriminators, determining a weight associated with each of the plurality of discriminators based on a distance between each discriminator and the set of features, generating an image info-graph associated with the first image generated by the MLD GAN upon receiving the first image, calculating a relevance associated with each of the plurality of discriminators based on the image info-graph, the set of features and the distance, and generating a plurality of images representing a plurality of emoticons associated with the one or more fictional characters based on each of the multiple sets of features.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation application, claiming priority under§ 365(c), of an International application No. PCT/KR2022/008242, filedon Jun. 10, 2022, which is based on and claims the benefit of an Indianpatent application number 202111026134, filed on Jun. 11, 2021, in theIndian Intellectual Property Office, the disclosure of which isincorporated by reference herein in its entirety.

TECHNICAL FIELD

The disclosure relates to generating one or more emoticons for one ormore users. More particularly, the disclosure relates to generating theone or more emoticons for the one or more users through a multiplelocalized discriminator (MLD) generative adversarial network (GAN).

BACKGROUND

With the advent of technology, various techniques for improving user'sexperience of providing inputs via touch-based keyboards that areimplemented in computing devices, have been devised. Most of thesetechniques are majorly focused on saving user's time, for example, byproviding suggestions for word completion, next word suggestions, andsuggestions for auto-correction, or in some cases graphics interchangeformat (GIF) suggestions.

Traditionally, ways of generating emoticons are either from image or byselecting each physical appearance feature manually. Story charactershave certain properties that are distinguishing features in theirappearances like facial features, a physique, a hairstyle, a spectacletype, an accessory (i.e., a hat or a piercing), or the like. All thefeature needs to be added or modified by user manually to resemble agiven story character and the process is labor intensive. A user canonly add the features that are available in the library. No option forcustom feature addition. There is no existing way to add or modifyelements to other user's emoticons.

Entertainment content in the form of online streaming or online books isgigantic these days. However, existing emoticons are all general-purposeand do not provide any correlation to story context and story specificelements.

Moreover, existing emoticons generations are either from image or byselecting each physical appearance feature manually. Story charactershave certain distinguishing features in their appearances like ahairstyle, a spectacle type, an accessory (i.e., a hat or a piercing),or the like. These features need to be added by user manually toresemble a given story character and the process is labor intensive.

The existing solutions create avatars of users which are general-purposeand can be used in on-going conversations. None of these provide anycorrelation to story context and story specific elements that can makeconversations quite playful.

The avatars are limited to matching physical appearance features ofusers. There is no inclusion of behavioral traits in any way whatsoever.

Fictional characters are available as avatars in Samsung's AR Emoji andApple's Animoji. However, these avatars directly represent somefictional character (like Mickey Mouse or a unicorn). They lack thefeature of styling user's avatar in fictional character's style. Forexample, instead of using Mickey Mouse avatar, user's avatar will haveears and nose like mickey mouse along with red pant outfit using thisdisclosure.

A company offers Friendmoji in which emoticons of a user and user'sfriend appear together in stickers. Others generate avatar for a givenuser and make items like stickers specific to given user. Since there isno correlation with story context, there is no mapping of fictionalcharacters and real-life characters (contact group of given user).

There is a need for a solution to overcome the above-mentioneddrawbacks.

The above information is presented as background information only toassist with an understanding of the disclosure. No determination hasbeen made, and no assertion is made, as to whether any of the abovemight be applicable as prior art with regard to the disclosure.

SUMMARY

Aspects of the disclosure are to address at least the above-mentionedproblems and/or disadvantages and to provide at least the advantagesdescribed below. Accordingly, an aspect of the disclosure is to providea selection of concepts in a simplified format that are furtherdescribed in the detailed description of the disclosure. This summary isnot intended to identify key or essential inventive concepts of theclaimed subject matter, nor is it intended for determining the scope ofthe claimed subject matter. In accordance with the purposes of thedisclosure, the disclosure as embodied and broadly described herein,describes method and system generating a one or more emoticons for oneor more users with respect to one or more fictional characters.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

In accordance with an aspect of the disclosure, a method for generatingone or more emoticons for one or more users with respect to one or morefictional characters is provided. The method includes, receiving, by aplurality of discriminators, a first image generated by a multiplelocalized discriminator (MLD) generative adversarial network (GAN) basedon a set of features from multiple sets of features associated with theone or more fictional characters, resulting in generation of an outputvalue associated with each of the plurality of discriminators. Themethod includes determining, by the plurality of discriminators, aweight associated with each of the plurality of discriminators based ona distance between each discriminator and the set of features. Themethod includes generating, by a pre-trained info-graph, an imageinfo-graph associated with the first image generated by the MLD GAN uponreceiving the first image. The method includes calculating, by a costcalculator, a relevance associated with each of the plurality ofdiscriminators based on the image info-graph, the set of features andthe distance. The method includes generating, by the MLD GAN, aplurality of images representing a plurality of emoticons associatedwith the one or more fictional characters based on each of the multiplesets of features. The method includes generating, by the MLD GAN, theone or more emoticons by styling one or more user images with respect toone or more images selected from the plurality of images, and one ormore user specific inputs.

In accordance with another aspect of the disclosure, a system forgenerating a one or more emoticons for one or more users with respect toone or more fictional characters is provided. The system includes aplurality of discriminators configured to receive a first imagegenerated by a MLD GAN based on a set of features from multiple sets offeatures associated with the one or more fictional characters, resultingin generation of an output value associated with each of the pluralityof discriminators. The plurality of discriminators is further configuredto determine a weight associated with each of the plurality ofdiscriminators based on a distance between each discriminator and theset of features. The system includes a pre-trained info-graph configuredto generate an image info-graph associated with the first imagegenerated by the MLD GAN upon receiving the first image. The system acost calculator configured to calculate a relevance associated with eachof the plurality of discriminators based on the image info-graph, theset of features and the distance. The system includes the MLD GANconfigured to generate a plurality of images representing a plurality ofemoticons associated with the one or more fictional characters based oneach of the multiple sets of features. The MLD GAN is further configuredto generate the one or more emoticons by styling one or more user imageswith respect to one or more images selected from the plurality ofimages, and one or more user specific inputs.

Other aspects, and advantages, and salient features of the disclosurewill become apparent to those skilled in the art from the followingdetailed description, which, taken in conjunction with the annexeddrawings, discloses various embodiments of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the disclosure will be more apparent from the followingdescription taken in conjunction with the accompanying drawings, inwhich:

FIGS. 1A and 1B illustrate an information graph with characters andcharacteristics in 2 dimensional word embedding according to variousembodiments of the disclosure;

FIG. 2A illustrates a flow diagram depicting a method for generating oneor more emoticons for one or more users with respect to one or morefictional characters according to an embodiment of the disclosure;

FIG. 2B illustrates a schematic block diagram 200 of a system forgenerating one or more emoticons for one or more users with respect toone or more fictional characters according to an embodiment of thedisclosure;

FIG. 3 illustrates an operational flow diagram depicting a method forgenerating one or more emoticons according to an embodiment of thedisclosure;

FIG. 4 illustrates an architecture depicting a method for generating oneor more emoticons for one or more users with respect to one or morefictional characters according to an embodiment of the disclosure;

FIG. 5 illustrates an architectural diagram depicting the info-graphgenerator according to an embodiment of the disclosure;

FIG. 6 illustrates an architectural diagram depicting an emoticongenerator engine according to an embodiment of the disclosure;

FIG. 7 illustrates an operational flow diagram depicting a process forselecting one or more features associated with one or more fictionalcharacters according to an embodiment of the disclosure;

FIG. 8 illustrates an operational flow diagram depicting a process forgenerating a number of images associated with one or more emoticonsaccording to an embodiment of the disclosure;

FIG. 9 illustrates an operational flow diagram depicting a process forgenerating one or more emoticons based on a genetic algorithm and themultiple localized discriminator (MLD) generative adversarial network(GAN) according to an embodiment of the disclosure;

FIG. 10 illustrates an operational flow diagram depicting a process forgenerating one or more emoticons for one or more users with respect toone or more fictional characters according to an embodiment of thedisclosure;

FIG. 11 illustrates a use case depicting generation of one or moreemoticons in style of one or more fictional character based onpersonality traits according to an embodiment of the disclosure;

FIG. 12 illustrates a use case depicting generation of one or moreemoticons in style of one or more fictional character according to anembodiment of the disclosure;

FIG. 13A illustrates an application use case depicting generationwallpapers and screensavers in style of one or more fictional characterbased on personality traits according to an embodiment of thedisclosure;

FIG. 13B illustrates an application use case associated with one or morechat wallpapers according to an embodiment of the disclosure;

FIG. 14A illustrates an application use case depicting character basedone or more emoticon generation for chat, stickers and GraphicsInterchange Format (GIFs) according to an embodiment of the disclosure;

FIG. 14B illustrates an application use case depicting context-basedchat stickers and GIFs matching a response from a sender according to anembodiment of the disclosure;

FIG. 14C illustrates an application use case depicting context-basedchat stickers and GIFs matching a response from a sender according to anembodiment of the disclosure;

FIG. 15 illustrates an application use case depicting generation ofcontact photos in style of one or more fictional characters according toan embodiment of the disclosure;

FIG. 16 illustrates an application use case depicting generation of oneor more emoticons for video calling and social media stories accordingto an embodiment of the disclosure;

FIG. 17 illustrates an application use case for generation of digitalnotifications based on one or more emoticons according to an embodimentof the disclosure;

FIG. 18 illustrates an application use case depicting generation of oneor more emoticons for system settings according to an embodiment of thedisclosure;

FIG. 19 illustrates an application use case depicting generation of oneor more emoticons for system settings according to an embodiment of thedisclosure; and

FIG. 20 illustrates a representative architecture to provide tools anddevelopment environment described herein for a technical-realization ofthe implementation in FIGS. 1A and 1B, 2A and 2B, 3 to 12, 13A and 13B,14A to 14C, and 15 to 19 through an AI model-based computing deviceaccording to an embodiment of the disclosure.

Further, skilled artisans will appreciate that elements in the drawingsare illustrated for simplicity and may not have been necessarily beendrawn to scale. For example, the flow charts illustrate the system interms of the most prominent operations involved to help to improveunderstanding of aspects of the disclosure. Furthermore, in terms of theconstruction of the device, one or more components of the device mayhave been represented in the drawings by symbols of the related art, andthe drawings may show only those specific details that are pertinent tounderstanding the embodiments of the disclosure so as not to obscure thedrawings with details that will be readily apparent to those of ordinaryskill in the art having benefit of the description herein according tothe related art.

Throughout the drawings, like reference numerals will be understood torefer to like parts, components, and structures.

DETAILED DESCRIPTION

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of variousembodiments of the disclosure as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the various embodiments describedherein can be made without departing from the scope and spirit of thedisclosure. In addition, descriptions of well-known functions andconstructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of thedisclosure. Accordingly, it should be apparent to those skilled in theart that the following description of various embodiments of thedisclosure is provided for illustration purpose only and not for thepurpose of limiting the disclosure as defined by the appended claims andtheir equivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces.

It will be understood by those skilled in the art that the foregoinggeneral description and the following detailed description areexplanatory of the disclosure and are not intended to be restrictivethereof.

Reference throughout this specification to “an aspect”, “another aspect”or similar language means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the disclosure. Thus, appearances of thephrase “in an embodiment”, “in another embodiment” and similar languagethroughout this specification may, but do not necessarily, all refer tothe same embodiment.

The terms “comprises”, “comprising”, or any other variations thereof,are intended to cover a non-exclusive inclusion, such that a process orsystem that comprises a list of operations does not include only thoseoperations but may include other operations not expressly listed orinherent to such process or system. Similarly, one or more devices orsub-systems or elements or structures or components proceeded by“comprises . . . a” does not, without more constraints, preclude theexistence of other devices or other sub-systems or other elements orother structures or other components or additional devices or additionalsub-systems or additional elements or additional structures oradditional components.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skilledin the art to which this disclosure belongs. The system, systems, andexamples provided herein are illustrative only and not intended to belimiting.

Embodiments of the disclosure subject matter are described below withreference to the accompanying drawings.

Generative Adversarial Networks (GANs for short) are generative models,meaning that GANs are used to generate new realistic data from theprobability distribution of the data in a given dataset. A GAN is amachine learning model, and more specifically a certain type of deepneural networks. An multiple localized discriminator (MLD) GAN may be amethod of training a GAN in a distributed fashion, that is to say overthe data of a set of participating workers (e.g., datacenters connectedthrough WAN, or devices at the edge of the Internet). MLD GAN is a newmodification in the GAN model that utilizes the classification power ofmultiple discriminator models instead of a single discriminator model.Also, the localization of discriminator models enables the model to havea better sense of classification for the output which is in it's clusteras compared to the rest. When the scope of output is diverse, then theGANs do not perform well because the discriminators have to classify awide range of diverse outputs. For this purpose, if MLDs are used, theMLDs will improve the classification, as the discriminators will not beclassifying diverse outputs. Initially, an information-graph(info-graph)has be generated by putting all the characters and characteristics in Ndimensional word embedding space for localizing the multiplediscriminators.

FIGS. 1A and 1B illustrate an information graph with characters andcharacteristics in 2 dimensional word embedding according to variousembodiments of the disclosure.

Referring to FIG. 1A, it illustrates an information graph withcharacters and characteristics in 2 dimensional word embedding accordingto an embodiment of the disclosure. The localized discriminators may begood at identifying certain types of images but not good at identifyingother types of images. Therefore, if the word embedding model generatesa word embedding graph in N dimensions, the M clusters are created incomplete word embedding space along with a local discriminator Di foreach cluster (i).

Referring to FIG. 1B, it illustrates clusters in word embedding spacewith local discriminators for each cluster according to an embodiment ofthe disclosure. The training part of MLD GAN will be further explainedlater referring to FIG. 8 .

FIG. 2A illustrates a flow diagram depicting a method for generating oneor more emoticons for one or more users with respect to one or morefictional characters according to an embodiment of the disclosure.

Referring to FIG. 2A, a method 100 may be configured to generate the oneor more emoticons through the multiple localized discriminator (MLD)generative adversarial network (GAN) and a genetic algorithm.

In accordance with an embodiment of the disclosure, the method 100includes receiving at operation 102, by a plurality of discriminators, afirst image generated by an MLD GAN based on a set of features frommultiple sets of features associated with the one or more fictionalcharacters, resulting in generation of an output value associated witheach of the plurality of discriminators. The features may becharacteristics such as ‘smart’, ‘fat’, intelligent’, etc.

Furthermore, the method includes determining at operation 104, by theplurality of discriminators, a weight associated with each of theplurality of discriminators based on a distance between eachdiscriminator and the set of features.

Moving forward, the method includes generating at operation 106, by apre-trained info-graph, an image info-graph associated with the firstimage generated by the MLD GAN upon receiving the first image.

Continuing with the above embodiment of the disclosure, the method 100includes calculating at operation 108, by a cost calculator, a relevanceassociated with each of the plurality of discriminators based on theimage info-graph, the set of features and the distance.

Furthermore, the method 100 includes generating at operation 110, by theMLD GAN, a plurality of images representing a plurality of emoticonsassociated with the one or more fictional characters based on each ofthe multiple sets of features.

In continuation with the above embodiment of the disclosure, the method100 includes generating at operation 112, by the MLD GAN, the one ormore emoticons by styling one or more user images with respect to one ormore images selected from the plurality of images, and one or more userspecific inputs.

FIG. 2B illustrates a schematic block diagram 200 of a system 202 forgenerating one or more emoticons for one or more users with respect toone or more fictional characters according to an embodiment of thedisclosure.

Referring to FIG. 2B, in an embodiment of the disclosure, the system 202may be incorporated in a user equipment (UE). Examples of the UE mayinclude, but are not limited to a laptop, a tab, a smart phone, apersonal computer (PC). In an embodiment of the disclosure, the one ormore fictional characters may be based one or more of a story, aconversation, a textual input, and a voice input. Further, the system202 may be configured to generate the one or more emoticons by a MLD GANbased on a fictional character info-graph, one or more user specificinputs, and one or more images associated with the one or more users.Details of the above aspects performed by the system 202 shall beexplained below.

The system 202 includes a processor 204, a memory 206, data 208, module(s) 210, resource (s) 212, a display unit 214, an info-graph generator216, a natural language processing (NLP) processor 218, an artificialintelligence (AI) processor 220, a trait comparator 222, an emoticongeneration engine 224, a feature selector 226, an emoticon generator228, a feature closeness calculator engine 230, a MLD GAN 232, a numberof discriminators 234, a pre-trained info-graph 236, a cost calculator238, and a weight update engine 240. In an embodiment of the disclosure,the processor 204, the memory 206, the data 208, the module (s) 210, theresource (s) 212, the display unit 214, the info-graph generator 216,the NLP processor 218, the AI processor 220, the trait comparator 222,the emoticon generation engine 224, the feature selector 226, theemoticon generator 228, the feature closeness calculator engine 230, theMLD GAN 232, the number of discriminators 234, the pre-trainedinfo-graph 236, the cost calculator 238, and the weight update engine240 may be communicatively coupled to one another. All or at least oneof the module 210, the info-graph generator 216, NLP processor 218, AIprocessor 220, the trait comparator 222, the emoticon generation engine224 may be combined into the processor 204.

At least one of the plurality of modules may be implemented through anAI model. A function associated with AI may be performed through thenon-volatile memory or the volatile memory, and/or the processor.

The processor may include one or a plurality of processors. At thistime, one or a plurality of processors may be a general purposeprocessor, such as a central processing unit (CPU), an applicationprocessor (AP), or the like, a graphics-only processing unit, such as agraphics processing unit (GPU), a visual processing unit (VPU), and/oran AI-dedicated processor, such as a neural processing unit (NPU).

A plurality of processors control the processing of the input data inaccordance with a predefined operating rule or artificial intelligence(AI) model stored in the non-volatile memory or the volatile memory. Thepredefined operating rule or artificial intelligence model is providedthrough training or learning. Here, being provided through learningmeans that, by applying a learning technique to a plurality of learningdata, a predefined operating rule or AI model of a desiredcharacteristic is made. The learning may be performed on a device itselfin which AI according to an embodiment is performed, and/or may beimplemented through a separate server/system. The AI model may consistof a plurality of neural network layers. Each layer has a plurality ofweight values, and performs a layer operation through calculation of aprevious layer and an operation of a plurality of weights. Examples ofneural networks include, but are not limited to, convolutional neuralnetwork (CNN), deep neural network (DNN), recurrent neural network(RNN), restricted Boltzmann Machine (RBM), deep belief network (DBN),bidirectional recurrent deep neural network (BRDNN), generativeadversarial networks (GAN), and deep Q-networks.

The learning technique is a method for training a predetermined targetdevice (for example, a robot) using a plurality of learning data tocause, allow, or control the target device to make a determination orprediction. Examples of learning techniques include, but are not limitedto, supervised learning, unsupervised learning, semi-supervisedlearning, or reinforcement learning.

According to the present subject matter, in a method of an electronicdevice, a method of generating one or more emoticons associated with oneor more users with respect to one or more fictional characters, by usingimage data as input data for an artificial intelligence model. Theartificial intelligence model may be obtained by training. Here,“obtained by training” means that a predefined operation rule orartificial intelligence model configured to perform a desired feature(or purpose) is obtained by training a basic artificial intelligencemodel with multiple pieces of training data by a training technique. Theartificial intelligence model may include a plurality of neural networklayers. Each of the plurality of neural network layers includes aplurality of weight values and performs neural network computation bycomputation between a result of computation by a previous layer and theplurality of weight values.

Visual understanding is a technique for recognizing and processingthings as does human vision and includes, e.g., object recognition,object tracking, image retrieval, human recognition, scene recognition,3D reconstruction/localization, or image enhancement.

As would be appreciated, the system 202, may be understood as one ormore of a hardware, a software, a logic-based program, a configurablehardware, and the like. In an example, the processor 204 may be a singleprocessing unit or a number of units, all of which could includemultiple computing units. The processor 204 may be implemented as one ormore microprocessors, microcomputers, microcontrollers, digital signalprocessors, central processing units, processor cores, multi-coreprocessors, multiprocessors, state machines, logic circuitries,application-specific integrated circuits, field-programmable gate arraysand/or any devices that manipulate signals based on operationalinstructions. Among other capabilities, the processor 204 may beconfigured to fetch and/or execute computer-readable instructions and/ordata stored in the memory 206.

In an example, the memory 206 may include any non-transitorycomputer-readable medium known in the art including, for example,volatile memory, such as a static random access memory (SRAM) and/or adynamic random access memory (DRAM), and/or a non-volatile memory, suchas a read-only memory (ROM), erasable programmable ROM (EPROM), flashmemory, hard disks, optical disks, and/or magnetic tapes. The memory 206may include the data 208. The data 208 serves, amongst other things, asa repository for storing data processed, received, and generated by oneor more of the processor 204, the memory 206, the data 208, the module(s) 210, the resource (s) 212, the display unit 214, the info-graphgenerator 216, the NLP subsystem 218, the AI processor 220, the traitcomparator 222, the emoticon generation engine 224, the feature selector226, the emoticon generator 228, the feature closeness calculator engine230, the MLD GAN 232, the number of discriminators 234, the pre-trainedinfo-graph 236, the cost calculator 238, and the weight update engine240.

The module(s) 210, amongst other things, may include routines, programs,objects, components, data structures, or the like, which performparticular tasks or implement data types. The module(s) 210 may also beimplemented as, signal processor(s), state machine(s), logiccircuitries, and/or any other device or component that manipulatesignals based on operational instructions.

Further, the module(s) 210 may be implemented in hardware, asinstructions executed by at least one processing unit, e.g., processor204, or by a combination thereof. The processing unit may be ageneral-purpose processor that executes instructions to cause thegeneral-purpose processor to perform operations or, the processing unitmay be dedicated to performing the required functions. In another aspectof the disclosure, the module(s) 210 may be machine-readableinstructions (software) which, when executed by a processor/processingunit, may perform any of the described functionalities.

In some example embodiments of the disclosure, the module(s) 210 may bemachine-readable instructions (software) which, when executed by aprocessor 204/processing unit, perform any of the describedfunctionalities.

The resource(s) 212 may be physical and/or virtual components of thesystem 202 that provide inherent capabilities and/or contribute towardsthe performance of the system 202. Examples of the resource(s) 212 mayinclude, but are not limited to, a memory (e.g., the memory 206), apower unit (e.g., a battery), a display unit (e.g., the display unit214) or the like. The resource(s) 212 may include a power unit/batteryunit, a network unit, or the like, in addition to the processor 204, andthe memory 206.

The display unit 214 may display various types of information (forexample, media contents, multimedia data, text data, or the like) to thesystem 202. The display unit 214 may include, but is not limited to, aliquid crystal display (LCD), a light-emitting diode (LED) display, anorganic LED (OLED) display, a plasma cell display, an electronic inkarray display, an electronic paper display, a flexible LCD, a flexibleelectrochromic display, and/or a flexible electrowetting display.

In an example, the info-graph generator 216, the NLP processor 218, theAI processor 220, the trait comparator 222, the emoticon generationengine 224, the feature selector 226, the emoticon generator 228, thefeature closeness calculator engine 230, the MLD GAN 232, the number ofdiscriminators 234, the pre-trained info-graph 236, the cost calculator238, and the weight update engine 240, amongst other things, includeroutines, programs, objects, components, data structures, or the like,which perform particular tasks or implement data types. The info-graphgenerator 216, the NLP processor 218, the AI processor 220, the traitcomparator 222, the emoticon generation engine 224, the feature selector226, the emoticon generator 228, the feature closeness calculator engine230, the MLD GAN 232, the number of discriminators 234, the pre-trainedinfo-graph 236, the cost calculator 238, and the weight update engine240 may also be implemented as, signal processor(s), state machine(s),logic circuitries, and/or any other device or component that manipulatesignals based on operational instructions. Further, the info-graphgenerator 216, the NLP processor 218, the AI processor 220, the traitcomparator 222, the emoticon generation engine 224, the feature selector226, the emoticon generator 228, the feature closeness calculator engine230, the MLD GAN 232, the number of discriminators 234, the pre-trainedinfo-graph 236, the cost calculator 238, and the weight update engine240 can be implemented in hardware, instructions executed by aprocessing unit, or by a combination thereof. The processing unit cancomprise a computer, a processor, such as the processor 204, a statemachine, a logic array or any other suitable devices capable ofprocessing instructions. The processing unit can be a general-purposeprocessor which executes instructions to cause the general-purposeprocessor to perform the required tasks or, the processing unit can bededicated to perform the required functions.

In an embodiment of the disclosure, the info-graph generator 216configured to generate a fictional character info-graph associated withthe one or more fictional characters based on a number of featuresassociated with the one or more fictional characters. Furthermore, theinfo-graph generator 216 may be configured to generate a user info-graphassociated with the one or more users based on a number of attributesassociated with the one or more users. Moving forward, the info-graphgenerator 216 may be configured to map the one or more fictionalcharacters with the one or more users based on the number of featuresand the number of attributes. The fictional character info-graph may bea graph with plotting of fictional characters on an N dimensional vectorspace filled with various words such as “tall”, “strong”, “fact”, etc.and the user info-graph may be a graph with plotting of features of theuser on the N dimensional vector space filled with various words.

In an embodiment of the disclosure, the info-graph generator 216 mayinclude the NLP processor 218, the AI processor 220, and the traitcomparator 222. In an embodiment of the disclosure, the NLP processor218 may be configured to generate a fictional character info-graph byanalyzing one or more conversations and one or more dialogue exchangesbetween the one or more fictional characters with respect to an eventnode of a plot graph associated with the one or more fictionalcharacters. In an embodiment of the disclosure, the plot graph may be arepresentation of a space of a number of possible stories associatedwith a scenario. Furthermore, the NLP processor 218 may be configured todetermine the number of features upon analyzing the one or moreconversations and one or more dialogue exchanges. In an embodiment ofthe disclosure, the number of features may include an interactionprofile from one or more event nodes shared by the one or more fictionalcharacters, an emotional pallet associated with the one or morefictional characters describing a level of expression of one or moreemotions experienced by the one or more fictional characters, and one ormore physical traits based on a textual description from the plot graphand one or more personality traits from the interaction profile and theemotion pallet for the one or more fictional characters. The interactionprofile may represent a set of collected features of the user which aredetermined after analyzing the interaction of the user with other usersvia texts, calls, or etc. The user's interaction with various otherevents may be, for example, a user's response to a feedback survey, aquestionnaire, and etc. In an embodiment of the disclosure, theemotional pallet may mean a set of emotional features of a person andthe strength—the level of expression—of the features. The level ofexpression indicates the strength of expression. For example, theexpression of “happy” is a low level (or the first level) of expressionand “very happy” is a high level (or the second level) of expression.

In an embodiment of the disclosure, the NLP processor 218 may beconfigured to generate a user info-graph by analyzing one or moreconversations and one or more dialogue exchanges between the one or moreusers with respect to an event node of a plot graph associated with theone or more users and one or more social media activities of the one ormore users. The NLP processor 218 may be configured to determine thenumber of attributes upon analyzing the one or more conversations andone or more dialogue exchanges. In an embodiment of the disclosure, thenumber of attributes may include an interaction profile from one or moreevent nodes shared by the one or more users, an emotional palletassociated with the one or more users describing a level of expressionof one or more emotions experienced by the one or more users, and one ormore physical traits based on a textual description from the plot graphand one or more personality traits from the interaction profile and theemotional pallet for the user.

Further, the AI processor 220 may be configured to parse one or more ofan audio input and a visual input to a textual form. Furthermore, the AIprocessor 220 may be configured to share the textual form with the NLPprocessor 218 for determining the interaction profile, the emotionalpallet, and the one or more physical traits and the one or morepersonality traits. In an embodiment of the disclosure, parsing mayinclude processing of an image, an audio, a video, and a multimediacontent received in the form of the one or more of the audio input, andthe visual input.

Continuing with the above embodiment of the disclosure, the traitcomparator 222 may be configured to map the one or more fictionalcharacters with the one or more users by calculating, a distance betweenthe number of features and the number of attributes. Furthermore, thetrait comparator 222 may be configured to create a mapping associatedwith the one or more users mapped to the one or more fictional characterbased on the distance. In an embodiment of the disclosure, the one ormore users may be mapped to the one or more fictional characters. In anembodiment of the disclosure, the number of features associated with theone or more fictional characters and the number of attributes associatedwith the one or more users may be selected from a content database by auser. The attributes may be called as categories such as personalitytraits, physical appearance traits, conversational traits, etc. Each ofthe traits may be further explained by features such as ‘tall’, ‘short’,‘fat’, ‘confident’, ‘intelligent’, etc. Each attribute may be made up ofsome features. Thus, the average of the attribute can be calculated as amean value or a value corresponding to the center position of thefeatures. Here, each feature can be represented by a vector which isdeveloped from the word embedding model. Once the vector representationof an attribute is calculated, the distance between the feature and theattribute can be obtained as the distance between the vectorrepresentation of the feature and the vector representation of theattribute.

In an embodiment of the disclosure, the emoticon generation engine maybe configured to select the one or more features from the number offeatures and generate the one or more emoticons associated with the oneor more users. In an embodiment of the disclosure, the emoticongeneration engine may incorporate the feature selector 226 and theemoticon generator 228. In an embodiment of the disclosure, the featureselector 226 may be configured to receive the fictional characterinfo-graph comprising the number of features associated with the one ormore fictional characters. Furthermore, the feature selector 226 may beconfigured to select one or more features from the number of featuresuniquely defining the one or more fictional characters and transmittingthe set of features. In an embodiment of the disclosure, the featureselector 226 may be configured to select the one or more features basedon determining a character closeness ratio representing a closenessbetween the one or more fictional characters based on a distance betweenthe one or more fictional characters. In an embodiment of thedisclosure, the feature selector 226 may include a feature closenesscalculator engine 230 configured to determine the character closenessratio. In an embodiment of the disclosure, the feature closenesscalculator engine 230 may be a reinforcement-based engine.

The calculation of the distance between the characters is explained indetail as follows. Every character may have a certain set of featuresdescribing the user. The character will have a certain degree ofattachment with every feature. For example, a character of ‘superman’may be described by features of ‘strong’, ‘fast’, ‘alien’, ‘good’,‘hope’, etc. And the superman character has a degree of attachment of,e.g., 90% for strong, 95% for hope, 60% for alien, etc. This degree ofattachment is basically how people will relate the fictional characterwith the keyword and can be derived by careful analysis of a story. Oncethe degree of attachment is obtained, it can be said that the character‘superman’ can be represented by average of all the features. Since allthe features are vectors obtained via word embedding model, so a vectorrepresenting the character can be obtained asSUPERMAN={0.9*V(strong)+0.95*V(hope)+0.6*V(alien)+ . . .}/{0.9+0.95+0.6+ . . . }. Once the vector representation of thefictional character is obtained, the distance between the characters canbe calculated. Here, the distance between the characters may be thedistance between the vector representations of the characters.

In an embodiment of the disclosure, the feature selector 226 may beconfigured to receive a user input as one of an audio input and atextual input. Furthermore, the feature selector 226 may be configuredto select the one or more features from the fictional characterinfo-graph based on the user input. In an embodiment of the disclosure,the one or more features may uniquely define the one or more fictionalcharacters.

Moving forward, the emoticon generator 228 may be configured to generatethe number of images upon receiving a multiple sets of featuresgenerated from the one or more features associated with the one or morefictional characters.

In an embodiment of the disclosure, the emoticon generator 228 mayinclude the MLD GAN 232, the number of discriminators 234, thepre-trained info-graph 236, and the cost calculator 238. Moving forward,the MLD GAN 232 may be configured to receive the one or more featuresdivided into the multiple sets of features. In an embodiment of thedisclosure, the MLD GAN 232 may be configured to generate an image foreach of the multiple set of features associated with the one or morefictional characters. In an embodiment of the disclosure, the image maybe referred as a first image.

Continuing with the above embodiment of the disclosure, the MLD GAN 232may be configured to transmit the first image to the number ofdiscriminators 234. Upon receiving the first image, the number ofdiscriminators 234 may be configured to generate an output valueassociated with each of the number of discriminators 234.

To that understanding, the number of discriminators 234 may beconfigured to determine a weight associated with each of the number ofdiscriminators 234 based on a distance between each discriminator andthe set of features. In a normal discriminator network, the weights forthe feedback is set to 1. When the discriminator correctly orincorrectly classifies an input of the user, the feedback is providedback to the discriminator. However, in an embodiment of the disclosure,since the discriminators are localized, the discriminators should bebetter trained for the input so that the feedback is provided not infull but in ratio of a distance from features.

Furthermore, a weight update engine 240 may be configured to receive adistance of each discriminator from the set of features and generate anupdated weight associated with the discriminator and the MLD GAN 232.

Furthermore, the first image may be passed to the pre-trained info-graph236 by the number of discriminators 234. Upon receiving the first image,the pre-trained info-graph 236 may be configured to generate an imageinfo-graph associated with the first image generated by the MLD GAN 232.

Moving ahead, the cost calculator 238 may be configured to calculate arelevance associated with each of the number of discriminators 234 basedon the image info-graph, the set of features and the distance.

Upon calculation of the relevance, the MLD GAN 232 may be configured togenerate a number of images representing a number of emoticonsassociated with the one or more fictional characters based on each ofthe multiple sets of features. Furthermore, the MLD GAN 232 may beconfigured to generate the one or more emoticons by styling one or moreuser images with respect to one or more images selected from the numberof images, and one or more user specific inputs. In an embodiment of thedisclosure, the MLD GAN 232 may be configured to select the one or moreimages from the number of images based on the output value of adiscriminator associated with the one or more images from the number ofdiscriminators 234. In an embodiment of the disclosure, the one or moreuser specific inputs may include a number of facial features, a facialtone, a hair color, a body built, a dressing style, one or moreaccessories associated with the one or more users. In an embodiment ofthe disclosure, styling the one or more user images comprisessuper-imposing the one or more images generated from the set of featuresassociated with the one or more fictional characters onto the one ormore user images and applying the one or more user specific inputsthereby generating the one or more emoticons.

In an embodiment of the disclosure, the cost calculator 238 may furtherbe configured to calculate a confidence score associated with the numberof discriminators 234 based on a number of parameters associated witheach of the number of discriminators 234. In an embodiment of thedisclosure, the number of parameters may include a weight, the outputvalue, and the relevance associated with each of the number ofdiscriminators 234. Furthermore, the cost calculator 238 may beconfigured to select remaining sets of features after generation of thefirst image based on the confidence score associated with the number ofdiscriminators 234. In an embodiment of the disclosure, a set offeatures corresponding to a discriminator with a high relevance amongthe number of discriminators 234 is prioritized for selection.

FIG. 3 illustrates an operational flow diagram 300 depicting a methodfor generating one or more emoticons according to an embodiment of thedisclosure.

Referring to FIG. 3 , in an embodiment of the disclosure, the method mayinclude enhancing a user's story experience by generating one or moreemoticons for the user and one or more contacts associated with the userin a style of fictional characters belonging to the story. In anembodiment of the disclosure, the user and the one or more contacts maybe one among the one or more user as referred in FIG. 2B. In anembodiment of the disclosure, the fictional characters may be referredto as one or more fictional characters as shown in FIG. 2B. The methodmay be configured to map one or more personality and behavioral traitsof the characters to one or more personality and behavioral traits ofthe one or more users to generate avatars in a meaningful way. In anembodiment of the disclosure, the one or more emoticons may be based onthe avatars.

In an embodiment of the disclosure, the method may include creating aninfo-graph of the traits and the physical features of the characters.Further, the method may include matching the info-graph associated withthe character with an info-graph of the one or more users using a socialmedia activity and a chat information of the one or more users. In anembodiment of the disclosure, the info-graph associated with thecharacter may be referred as the one or more fictional character and theinfo-graph associated with the one or more users may be referred as ause info-graph as referred in FIG. 2B.

Upon mapping, corresponding emoticons from the one or more emoticons maybe generated using the MLD GAN 232 along with the genetic algorithm.Furthermore, a relevance of each discriminator may be dynamicallycalculated to incorporate features from required clusters and enhancegenerated population of the one or more emoticons. In an embodiment ofthe disclosure, each discriminator may be related to a cluster.Furthermore, each of the discriminators may be referred as the number ofdiscriminators 234 as referred in FIG. 2B. The generatedcharacter-styled one or more emoticons may be used as one or more ofstickers, GIFs, and wallpapers.

FIG. 4 illustrates an architecture depicting a method for generating oneor more emoticons for one or more users with respect to one or morefictional characters according to an embodiment of the disclosure.

Referring to FIG. 4 , the method may be configured to generate the oneor more emoticons through the MLD GAN 232 and a genetic algorithm.

In an embodiment of the disclosure, an architecture 400 may include theinfo-graph generator 216, a contacts-character mapping and an emoticongeneration module. In an embodiment of the disclosure, thecontacts-character mapping may be a mapping between the one or morefictional characters and the one or more users. In an embodiment of thedisclosure, the characters may be the one or more fictional charactersas referred in FIG. 2B and the contacts may be referred as the one ormore users.

Furthermore, the info-graph generator 216 may be configured to create aword-embedding for the physical and personality traits for the characterand the contact of the user. In an embodiment of the disclosure, thephysical and personality traits associated with the character may bereferred as a number of features and the physical and personality traitsassociated with the contact may be referred as a number of attributes asreferred in FIG. 2B.

Generation of a fictional character info-graph includes extraction ofcontent data, such as textual data, audio data and visual data from acontent database. Generation of the fictional character info-graphfurther includes creation of a plot graph by representing a story ascombination of separate narratives and an interaction between thecharacters based on events in the story.

Generation of the fictional character info-graph further includes usingan NLP processor 218 to determine the interaction profile of thecharacter with other characters based on a dialogue exchange and anevent correlation. In an embodiment of the disclosure, the NLP processor218 may be referred as the NLP processor 218 as referred in FIG. 2B. TheNLP processor 218 may also create an emotion palette for the characterbased on the dialogues present in the content. From the content,physical characteristics of the character may be separated out.

Generation of the fictional character info-graph includes receivingcontent data present in a visual form to create the interaction profile,emotion palette and physical/personality traits for the character by theAI processing engine.

Generating a user info-graph may include extraction of a social mediaactivity and chats amongst the one or more users. Generating the userinfo-graph may include using the NLP processor 218 to assess a type ofrelationship with user based on a mutual sharing, comments and chatsamongst the one or more users. Generating a user info-graph may includeusing the NLP processor 218 to create a psychological profile of thecontact based on social media posts and comments. The psychologicalprofile may be information about the psychological traits of thecontact, which can be obtained by an analysis of the interactionsbetween the user and the contacts. The psychological profile mayinclude, but not limited to, features such as ‘influencer’,‘suspicious,’, ‘mentally strong’, ‘self-doubtfulness’, etc. Further,generating the user info-graph may include receiving gallery informationof the user to create a personality profile of the contact by the AIprocessing engine. The gallery information may include a collection ofmoving pictures and/or still pictures stored in the user's device.

Moving forward, the architecture 400 may include a contact character mapcalculator configured to compare a feature word embedding of thecharacters and the contacts generated from the info-graph generator 216.Further, the contact character map calculator may be configured to mapthe characters to the contacts whose personality traits vector areclosest to one another other. Further, the mapping may be used togenerate the one or more emoticons for the contacts in style of acertain character.

Furthermore, the architecture 400 may include the emoticon generatorengine 224 configured to receive the character and the contactsinfo-graph containing a word embedding representing the physicalcharacteristics and the personality traits for the contact and theassociated character. The one or more emoticon generation process may bebased on the feature selector 226 and the emoticon generator 228.

The feature selector 226 may receive an ‘N’ dimensional vector V={v1,v2, v3, . . . , vn} representing the word embedding of the physicalcharacteristics of the contacts in style of which the one or moreemoticons is to be generated. The N dimensional vector space may befilled with various words. The feature selector 226 may be configured toselect a user preferred ‘K’ features (‘K’ clusters or ‘K’ groups) froman ‘N’ dimensional vector such that the features selected may uniquelydistinguish the character from the cast. At least one point of referencefor each cluster corresponding to each features is assigned. Forexample, C point for C cluster corresponds to C feature—e.g., smart.Each discriminator is placed in each point of reference for eachcluster. Thus, K discriminators are assigned to K clusters and eachdiscriminators may classify the images in its cluster better as comparedto the images outside the cluster because when the discriminators aretrained, the weights of discriminator may be updated in such a way thatthe discriminator's weight becomes affected more by the correct orincorrect classifications of images placed near to the discriminatorthan the classifications of images far from the discriminator. In anembodiment of the disclosure, the ‘K’ features may be one or morefeatures as referred in FIG. 2B. Further, the cast may be one of the oneor more fictional characters. The feature selector 226 may preserve acloseness ratio to determine features of a specific character resemblinganother character in the story.

Further, the emoticon generator 228 may receive a set of physicalfeatures of the character and one or more images of the contact forwhich an avatar is to be created. The emoticon generator 228 may firstgenerate the one or more emoticons of the character based on thefeatures selected by a first representing the word-embedding in aN-Dimensional space and using a fusion of the MLD GAN 232 with a geneticalgorithm-based model including multiple discriminators to generate theone or more emoticons of the character with the selected features. Theone or more emoticons may be passed to a cycle GAN with the one or moreimages of the contact to generate the one or more emoticons of thecontact in style of a given character.

FIG. 5 illustrates an architectural diagram 500 depicting the info-graphgenerator 216 according to an embodiment of the disclosure.

Referring to FIG. 5 , in an embodiment of the disclosure, the info-graphgenerator 216 may be configured to create a word embedding for a numberof physical and personality traits for the characters and contacts. Inan embodiment of the disclosure, the number of physical and personalitytraits may be obtained based on one or more of a textual data, an audiodata and a visual data extracted from a database. In an embodiment ofthe disclosure, the character may be amongst one or more fictionalcharacters and the contact may be amongst one or more users as referredin FIG. 2B. In an embodiment of the disclosure, the number of physicaland personality traits related to the character may be a number offeatures as referred in FIG. 2B. Further, the number of physical andpersonality traits related to the contact may be a number of attributesas referred in FIG. 2B. Furthermore, the word embedding related to thecharacter may be the fictional character info-graph and the wordembedding related to the user may be a user info-graph as referred inFIG. 2B. Furthermore, for generating the word embedding of thecharacter, the info-graph generator 216 may receive a textualdescription present in one or more of a content, one or more dialogues,one or more facial expressions based on events to create a personalityprofile for the character.

For generation of word embedding of the personality traits of thecontact, the info-graph generator 216 may fetch social media activitydata including a content shared publicly, and a content shared withusers amongst the one or more users, and comments on social media postedby the contact and an interaction of the contact with the user and apersonal chat information of the contact.

In an embodiment of the disclosure, the info-graph generator 216 may beconfigured to generate a plot graph. In an embodiment of the disclosure,the plot graph may be a compact representation of a space of one or morepossible stories describe a certain scenario associated with the one ormore fictional characters. In an embodiment of the disclosure, the plotgraph may be configured to separates one or more individual narrativesof a character from the content database including events encountered bythe character, temporal orderings of the events and mutual exclusions.In an embodiment of the disclosure, the mutual exclusions may be one ormore scenarios unable to take place in a similar story. If theactivities of a user are monitored and mapped sequentially or theactivities of a fictional character are mapped to a story, then thismonitored or mapped activities may form a graph which will showtransitions from one state to another. This plot graph may depicttransitions from one state to another or put simply from one activity toanother.

In an embodiment of the disclosure, the event nodes may be one or morevertices of the plot graph. Each of the nodes represents one event.Further, each node may include a set of sentences semanticallydescribing the event learned from one or more crowdsourced exemplarstories.

Moving ahead, the temporal orderings may be one or more unidirectionaledges of the plot graph. The temporal orderings may be a partialordering indicating a necessary order for the events in the story. In anembodiment of the disclosure, if an event A is ordered before an eventB, the event B may not occur until the event A has occurred in thestory.

Continuing with the above embodiment of the disclosure, the mutualexclusions may be one or more bidirectional edges of the plot graph. Inan embodiment of the disclosure, the mutual exclusions may indicatesituations two events being unable to take place in the similar story.

In an embodiment of the disclosure, the info-graph generator 216 mayfurther include a natural language processing (NPL) system. In anembodiment of the disclosure, the NLP processor 218 may be configured toanalyze one or more dialogue exchanges with other characters and othercontacts and interactions with the other characters and the contactswith respect to the event nodes of the plot graph for each node of theplot graph for the character and the contact. Furthermore, the NLPprocessor 218 may be configured to determine a number of characteristicsassociated with the character and the contact.

In an embodiment of the disclosure, the number of characteristicsinclude an interaction profile, an emotional profile, and one or morephysical and personality traits. Furthermore, the interaction profilemay be extracted from the event nodes shared by two or more charactersand contacts. The interaction profile of the character may give anoverview of a relation with other characters and contacts. From theevent nodes, the personality of the character and the contact may beextracted.

Continuing with the above embodiment of the disclosure, the emotionalpallet may focus on creating an emotional pallet for the character andthe contact describing a level of expression of a particular emotionwhile experiencing one or more of a certain situation and an interactionwith the other character and contact.

Moving forward, the physical traits of the character may be determinedfrom the textual description provided from the plot graph of a certaincharacter. The personality traits may be determined from the interactionprofile and the emotion pallet of the character and the contact.

In an embodiment of the disclosure, the info-graph generator 216 mayfurther include an AI processor 220. In an embodiment of the disclosure,the AI processor 220 may be configured to parse the audio and the visualinput to a textual form further provided to the NLP processor 218 todetermine the interaction profile, the emotional profile, and the one ormore physical and personality traits. Using a combination of the AIprocessor 220 and the NLP processor 218 with the plot graph, a wordembedding vector describing the personality and physical traits of thecharacter and the contact may be created.

Furthermore, the info-graph generator 216 may include a contactcharacter map calculator configured to determine a contact suitable tobe mapped to a character using the traits comparator as referred in FIG.2B. The trait comparator 222 may be configured to calculate a distancebetween a traits vector of the character with the one or more users tocreate a mapping of the one or more users to the character in the styleassociated with one or more avatars may be generated. In an embodimentof the disclosure, the one or more avatars may be based on the one ormore fictional characters.

FIG. 6 illustrates an architectural diagram 600 depicting an emoticongenerator engine 224 according to an embodiment of the disclosure.

Referring to FIG. 6 , in an embodiment of the disclosure, the emoticongenerator engine 224 may be configured to generate one or more emoticonsfor one or more users. In an embodiment of the disclosure, the one ormore emoticons may be developed in a style of some fictional character.In an embodiment of the disclosure, the fictional character may beamongst one or more fictional characters as referred in FIG. 2B.

Continuing with the above embodiment of the disclosure, the emoticongenerator engine 224 may include the feature selector 226 and theemoticon generator 228. Furthermore, the feature selector 226 mayinclude a feature closeness calculator module. In an embodiment of thedisclosure, the feature closeness calculator module may be areinforcement-based model with an objective to select user preferred ‘K’features from a ‘N’ dimensional vector. In an embodiment of thedisclosure, the ‘K’ features may be the one or more features amongst thenumber of features. In an embodiment of the disclosure, the ‘K’ featuresselected may be capable to uniquely distinguish the one or morefictional characters from one another.

In continuation with above embodiment of the disclosure, the emoticongenerator 228 may be configured to generate the one or more emoticonsassociated with the one or more users. In an embodiment of thedisclosure, the emoticon generator 228 may include the MLD GAN 232 aimedat generating a number of images for the one or more fictionalcharacters based on a genetic algorithm with the MLD GAN 232 to generatethe number of images for an unknown character based on characteristicsrelated to the unknown character. In an embodiment of the disclosure,the unknown character may be amongst the one or more fictionalcharacters. Furthermore, the characteristics may be the one or morefeatures related to the one or more fictional characters.

FIG. 7 illustrates an operational flow diagram 700 depicting a processfor selecting one or more features associated with one or more fictionalcharacters according to an embodiment of the disclosure.

Referring to FIG. 7 , in an embodiment of the disclosure, the one ormore features may be selected from a number of features associated withthe one or more fictional characters. In an embodiment of thedisclosure, the one or more features may be selected by the featureselector 226 as referred in FIG. 2B.

Continuing with the above embodiment of the disclosure, the process mayinclude, receiving, by the feature selector 226 an N dimensional vectorrepresenting one or more identifiable physical features related to theone or more fictional characters. In an embodiment of the disclosure,the N dimensional vector may be a fictional character info-graph asreferred in FIG. 2B.

Further, the process may include selecting a set of ‘K’ features suitingone or more users such that the set of ‘K’ features is distinguishableas the one or more fictional characters. In an embodiment of thedisclosure, the set of ‘K’ features may be referred as the one or morefeatures.

Moving forward, the process may include performing selection of the ‘K’features from the number of features based on a user preference suchthat a number of objectives are satisfied. In an embodiment of thedisclosure, the number of objectives may include the set of ‘K’ featuresis enough to distinguish the character from other characters in anarrative. In an embodiment of the disclosure, the characters may beamongst the one or more fictional characters. Further, the set of ‘K’features selected for style transfer may abide the user preferences fora given contact. In an embodiment of the disclosure, the one or moregiven contact may be amongst the one or more users.

In an embodiment of the disclosure, a universal set of features for acharacter ‘C’ may be Fc={Fc₁, Fc₂, Fc₃, . . . } and a charactercloseness ratio be R={1, 1, 1, . . . } initially. Further, for everyfeature (Fi), add features to feature set (Fs) if Σ(fc−fci)²/N>T.Further, if (fc−fc_(i))²<T, R_(i)=2*R_(i), for the number of features inFc, add feature Fi if (fc−fci)²>R_(i)*T. In an embodiment of thedisclosure, “T” may be referred as a pre-determined threshold. Further,passing the Feature List (Fs) to a reinforcement learning model to fetchthe user preferences. In an embodiment of the disclosure, thereinforcement module may be the reinforcement module as referred in FIG.6 .

FIG. 8 illustrates an operational flow diagram 800 depicting a processfor generating a number of images associated with one or more emoticonsaccording to an embodiment of the disclosure.

Referring to FIG. 8 , in an embodiment of the disclosure, the one ormore emoticons may be generated with respect to one or more fictionalcharacters. Furthermore, the one or more fictional characters may bebased on one or more of a story, a conversation, a textual input, and avoice input. In an embodiment of the disclosure, the process may includegeneration of an info-graph by putting all the character andcharacteristics in an N dimensional word embedding space. In anembodiment of the disclosure, the info-graph may be the fictionalcharacter info-graph associated with the one or more fictionalcharacters as referred in FIG. 2B. In an embodiment of the disclosure,the process may utilize localized discriminators for generating the oneor more emoticons. In an embodiment of the disclosure, the localizeddiscriminators may be the number of discriminators 234 as referred inFIGS. 2A and 2B. Each of the number of discriminators 234 may beconfigured to identify a specific type of images amongst a number ofimages.

In an embodiment of the disclosure, where a word embedding modelincludes N dimensions, N clusters in complete word embedding space alongwith a discriminator D_(i) locally for each cluster (i) are created.

In an embodiment of the disclosure, the MLD GAN 232 may receive anoise—feature vector—as an input and create an image I for training. Inan embodiment of the disclosure, the noise may be an attribute vector,such as one or more features amongst a number of features associatedwith the one or more fictional characters. Further, the image I may bepassed to each of the number of discriminators 234 such that eachdiscriminator (Di) may produce an output value (Vi). The output value(Vi) may denote the classification of the generated image as real forfake. Thus, (Di) produces (Vi).

Further, for weight updating part, a weight update engine 240 mayreceive a distance of each discriminator (Di) from the noise andgenerate an updated weight associated with the discriminator and the MLDGAN 232. The weight update engine 240 may measure the distance of Difrom the noise input. since every discriminator belongs to certaincluster in the input space. Thus, the discriminator may be representedas a point in the N dimensional graph. I.e., every discriminator willhave a vector representing its position in the input space. The noiseinput is the feature vector in this case. The weight update engine 240may update the weight of both of discriminator Di and the generator.Here, the distance between the discriminator and the noise input meansthe distance between the vector representation of the discriminator andthe input feature vector. Furthermore, each discriminator network may beconfigured to assess a generated image also referred as the image I withrespect to the properties of a corresponding cluster. Further, theweight may be updated in proportion to a relevance with the noise.

In an embodiment of the disclosure, the weight assigned to adiscriminator Di, is Wi based on the distance from the noise input, theweight update for the Di is done based on a loss L[Di]=Wi*Vi. The longerthe distance between the discriminator and the feature vector, the lessis the weight W. Furthermore, a combined average may be used for the MLDGAN 232 calculated by the equation mentioned below:

L[G]=λ*Σ_(t=1) ^(n)(Wi*Vi)/(Wi)  Equation 1

FIG. 9 illustrates an operational flow diagram 900 depicting a processfor generating one or more emoticons based on a genetic algorithm andthe MLD GAN 232 according to an embodiment of the disclosure.

Referring to FIG. 9 , initially, an info-graph may be generated byputting all the character and characteristics in an N dimensional wordembedding space. In an embodiment of the disclosure, the info-graph maybe the fictional character info-graph associated with the one or morefictional characters as referred in FIG. 2B.

In reference to the genetic algorithm, each attribute may be referred asa gene. In an embodiment of the disclosure, the attribute may be theattribute vector as referred in FIG. 8 . Furthermore, a combination of Kattributes may be referred as a chromosome such that each chromosome maybe the input to the MLD GAN 232. Further, a combination of thechromosomes may be referred as a population. In an embodiment of thedisclosure, the gene, the chromosomes, and the population may be storedat a DNA pool.

In an embodiment of the disclosure, T may be a text embeddingrepresenting the K attributes to be included in avatar image, whereinT={t1, t2, . . . , tk}. In an embodiment of the disclosure, the Kattributes may also be referred as K features.

In an embodiment of the disclosure, the process may include generating anumber of different inputs to be fed into the MLD GAN 232 by combiningthe K attributes out of N features in some random manner. In anembodiment of the disclosure, the K features may be referred as the oneor more features and the N features may be the number of features asreferred in FIG. 2B. In an embodiment of the disclosure, a group of anumber of inputs may generate a generation one of images. In anembodiment of the disclosure, the generation one of images may be basedon the number of images.

For every input in the form of the chromosome in a generation GQ forgenerating the number of images, an image may be created using agenerator network of the MLD GAN 232 model. Upon generation, the imagemay be passed through N discriminator networks. In an embodiment of thedisclosure, the N discriminator networks may be the number ofdiscriminators 234 as referred in FIG. 2B. In an embodiment of thedisclosure, a result of a discriminator amongst the number ofdiscriminators 234 may be Vi. In an embodiment of the disclosure, theresult may indicate whether the image is real or fake with respect to acorresponding cluster of each discriminator network. Further, the weightmay be provided to each discriminator as Wi by determining the distanceof the input feature vector T with the discriminator Di.

Continuing with the above embodiment of the disclosure, an output imageof the MLD GAN 232 for every C chromosome may be fed into a pre-trainedinfo-graph 236 generator 216 [CNN model]. The info-graph generator 216may be configured to generate a text embedding from the image, such thatthe embedding may be T′={t1′, t2′, . . . , tk′}.

In continuation with the above embodiment of the disclosure, theembedding T′ may be passed to the cost calculator 238 as referred inFIG. 2B. In an embodiment of the disclosure, the cost calculator 238 maybe configured to create a relevance associated with the output image. Inan embodiment of the disclosure, the relevance may be calculated basedon the equation mentioned below:

Relevance=Σ_(l=1) ^(k)(distance(ti,ti′)/(K)  Equation 2

Furthermore, the process may include generating a confidence score suchthat the confidence score is an indicator of a quality. Furthermore, theconfidence score may be used select chromosomes for making a nextgeneration of images for generating the number of images. In anembodiment of the disclosure, the confidence score may be defined as acomposite of discriminator results associated with the number ofdiscriminators 234 and the relevance calculated above. Further, theconfidence score may be calculated based on the equation mentionedbelow:

Confidence=λ1*(W ₁ V ₁ +W ₂ V ₂ + . . . +W ₃ V ₃)+λ₂*Relevance  Equation3

Furthermore, for every input of generation 1 of the images forgenerating the number of images, a tuple including the input noise, suchas the chromosome, the image, and the confidence score may be present.

For generating a second generation, a number of rules associated withthe genetic algorithm may be applied. In an embodiment of thedisclosure, the number of rules may include selecting top C/2chromosomes for next generation from the number of chromosomes. In anembodiment of the disclosure, the C/2 chromosomes may be the chromosomesused as the input for the number of discriminators 234 with a highestconfidence score amongst one another.

Further, the number of rules may include selecting top C/4 chromosomesupon changing one or more genes from the number of chromosomes forming amutated setting.

Continuing with the above embodiment of the disclosure, the number ofrules may include selecting top few chromosomes and making crossoversbetween the few chromosomes and selecting the top C/4 chromosomes of thecrossovers. Furthermore, the second generation may be further evaluated,and a third generation may be obtained from the number of chromosomes.Further, the process may repeat for multiple times generating Ggenerations of images. In an embodiment of the disclosure, the Ggenerations of images may constitute the number of images.

Further after G generations, a set of C noise inputs or chromosomesgenerating best images of the one or more fictional character may beused. A user may select one of the number of images for the one or morefictional characters. By default, an image with a best discriminatorscore may be selected.

FIG. 10 illustrates an operational flow diagram 1000 depicting a processfor generating one or more emoticons for one or more users with respectto one or more fictional characters according to an embodiment of thedisclosure.

Referring to FIG. 10 , in an embodiment of the disclosure, the one ormore emoticons may be generated based on the number of discriminators234 and the MLD GAN 232. Further, the one or more emoticons may be basedon a relevance score and a confidence score associated with the numberof discriminators 234.

At operation 1002, the process includes, generating a characterinfo-graph by mapping personality and physical traits of one or morefictional characters on an info-graph for a story. In an embodiment ofthe disclosure, the character info-graph may be a fictional characterinfo-graph as referred in FIG. 2B. Furthermore, the personality andphysical traits of the one or more fictional characters may be thenumber of features of the one or more fictional characters.

At operation 1004, the process includes generating a contact info-graphfor the one or more users by analyzing social media activities of theone or more users and communication amongst the one or more users. In anembodiment of the disclosure, the contact info-graph may be a userinfo-graph as referred in FIG. 2B.

At operation 1006, the process includes mapping the one or morefictional characters to the one or more users with closest match in thebehavioral traits and the personality traits using a distance metric.

At operation 1008, the process includes calculating the relevance ofeach discriminator based on the distance from a set of features in anN-D word-embedding space.

At operation 1010, the process includes generating an avatar offictional characters using traits from the character info-graph, the MLDGAN 232 and the genetic programming. In an embodiment of the disclosure,the avatar may be one or more emoticons.

At operation 1012, the process includes styling the avatar associatedwith the one or more users and the one or more fictional characters in astyle of corresponding mapped fictional characters' avatars generated inoperation 1010.

FIG. 11 illustrates a use case 1100 depicting generation of one or moreemoticons in style of one or more fictional character based onpersonality traits according to an embodiment of the disclosure.

Referring to FIG. 11 , in an embodiment of the disclosure, thepersonally trait may be the number of features associated with the oneor more fictional characters.

FIG. 12 illustrates a use case 1200 depicting generation of one or moreemoticons in style of one or more fictional character according to anembodiment of the disclosure.

Referring to FIG. 12 , at operation 1202, an avatar may be generatedbased on fictional characters.

At operation 1204, a number of features may be extracted associated withthe fictional character.

At operation 1206, an image may be created based on MLD GAN 232 avatarcreation model. In an embodiment of the disclosure, the MLD GAN 232avatar creation model may be the MLD GAN 232.

At operation 1208, a styling of the image may be performed.

At operation 1210, a final avatar may be created in the style ofcharacter.

FIG. 13A illustrates an application use case 1300 a depicting generationwallpapers and screensavers in style of one or more fictional charactersbased on personality traits according to an embodiment of thedisclosure.

Referring to FIG. 13A, in an embodiment of the disclosure, thepersonally trait may be the number of features associated with the oneor more fictional characters. In an embodiment of the disclosure, thewallpapers and the screensavers may be generated for one or more of asingle user and a group of friends. In an embodiment of the disclosure,the single user and the group of friends may be amongst one or moreusers. Wallpapers may be provided such that the user remains connectedto the story even in absence of new episodes and new books. In anembodiment of the disclosure, the wallpaper and the screen savers maydepict a glimpse of upcoming plot.

FIG. 13B illustrates an application use case 1300 b associated with oneor more chat wallpapers according to an embodiment of the disclosure.

Referring to FIG. 13B, in an embodiment of the disclosure, the chatWallpapers may be generated for one or more of a contact and a group offriends. In an embodiment of the disclosure, the contact and the groupof friends may be amongst one or more users. The chat wallpapers forindividual contact and even group depicts a kind of bond and experiencesshared by one or more fictional characters. The chat wallpapers changingdynamically with story plot depicts a kind of dynamics between the oneor more fictional characters.

FIG. 14A illustrates an application use case 1400 a depicting characterbased one or more emoticon generation for chat, stickers and graphicsinterchange format (GIFs), according to an embodiment of the disclosure.

Referring to FIG. 14A, in an embodiment of the disclosure, the charactermay be one or more fictional characters.

At operation 1402 a, a character emoticon may be made from a textdescription associated with one or more users.

At operation 1404 a, one or more emoticons associated with the one ormore users may be generated.

At operation 1406 a, one or more user emoticons in a style of charactermay be generated based on selected features related to the characters.

FIG. 14B illustrates an application use case 1400 b depictingcontext-based chat stickers and GIFs matching a response from a senderaccording to an embodiment of the disclosure.

Referring to FIG. 14B, a system may assess a response to determine if asticker may be generated using one or more fictional characters mappedto the sender. The chat stickers and the GIFs also contain one-linersfrom a story such that the one-liners may be relevant in the context ofthe chat.

FIG. 14C illustrates an application use case 1400 c depictingcontext-based chat stickers and GIFs matching a response from a senderaccording to an embodiment of the disclosure.

Referring to FIG. 14C, a system may assess a response to determine if asticker may be generated using one or more fictional characters mappedto the sender. The chat stickers may be based on one or more fictionalcharacters generated based on context.

FIG. 15 illustrates an application use case 1500 depicting generation ofcontact photos in style of one or more fictional characters according toan embodiment of the disclosure.

Referring to FIG. 15 , in an embodiment of the disclosure, a contactPhoto may be created in style of the one or more fictional charactersbased on personality traits.

FIG. 16 illustrates an application use case 1600 depicting generation ofone or more emoticons for video calling and social media storiesaccording to an embodiment of the disclosure.

Referring to FIG. 16 , the one or more emoticons in a style of one ormore fictional characters may be used during the video calls, stories,and social media posts.

FIG. 17 illustrates an application use case 1700 for generation ofdigital notifications based on one or more emoticons accordance to anembodiment of disclosure.

Referring to FIG. 17 , in an embodiment of the disclosure, the digitalnotifications may be digital well-being notifications. The one or moreemoticons related to a user in a fictional character style may be usedas subtle digital-wellbeing notifications.

At operation 1702, the user is watching TV for long time.

At operation 1704, a Tele Vision notifies the user to have dinner usingthe one or more fictional characters in fictional character style.

FIG. 18 illustrates an application use case 1800 depicting generation ofone or more emoticons for system settings according to an embodiment ofthe disclosure.

Referring to FIG. 18 , in an embodiment of the disclosure, the one ormore emoticons generated may be used with a number of system settings ofa Tele Vision and mobile phones.

Using the one or more emoticons with settings may make UI/UX fun andeventful further helping in conveying a better sense of clarity andpurpose of a number of existing settings. The one or more emoticons maybe used with the one or more emoticons animation and a text to coveraspects of the setting and assist the user in reaching desirableconfiguration. An animated may be used as a representative of a digitalbutler. The digital butler may be able to identify various nearbydevices and connect them to TV much efficiently like Spiderman is ableto grab nearby objects.

FIG. 19 illustrates an application use case 1900 depicting generation ofone or more emoticons for system settings according to an embodiment ofthe disclosure.

Referring to FIG. 19 , the sound settings may be assisted by an animatedcharacter emoticon with auditory senses. Picture settings may beassisted by another animated emoticon with great visual and analyticalskills.

FIG. 20 illustrates a representative architecture 2000 to provide toolsand development environment described herein for a technical-realizationof the implementation in preceding figures through a virtual personalassistance (VPA) based computing device according to an embodiment ofthe disclosure.

FIG. 20 is merely a non-limiting example, and it will be appreciatedthat many other architectures may be implemented to facilitate thefunctionality described herein. The architecture may be executing onhardware, such as a computing machine 202 of FIG. 2B that includes,among other things, processors, memory, and various application-specifichardware components.

Referring to FIG. 20 , an architecture 2000 may include anoperating-system, libraries, frameworks or middleware. The operatingsystem may manage hardware resources and provide common services. Theoperating system may include, for example, a kernel, services, anddrivers defining a hardware interface layer. The drivers may beresponsible for controlling or interfacing with the underlying hardware.For instance, the drivers may include display drivers, camera drivers,Bluetooth® drivers, flash memory drivers, serial communication drivers(e.g., universal serial bus (USB) drivers), Wi-Fi® drivers, audiodrivers, power management drivers, and so forth depending on thehardware configuration.

A hardware interface layer includes libraries which may include systemlibraries, such as file-system (e.g., C standard library) that mayprovide functions, such as memory allocation functions, stringmanipulation functions, mathematic functions, and the like. In addition,the libraries may include API libraries, such as audio-visual medialibraries (e.g., multimedia data libraries to support presentation andmanipulation of various media format, such as moving picture expertsgroup 4 (MPEG)4, H.264, MPEG-1 or MPEG-2 audio layer 3 (MP3), advancedaudio coding (AAC), adaptive multi-rate (AMR), joint photographic group(JPG), portable graphics format (PNG)), database libraries (e.g., SQLitethat may provide various relational database functions), web libraries(e.g., WebKit that may provide web browsing functionality), and thelike.

A middleware may provide a higher-level common infrastructure, such asvarious graphical user interface (GUI) functions, high-level resourcemanagement, high-level location services, and so forth. The middlewaremay provide a broad spectrum of other APIs that may be utilized by theapplications or other software components/modules, some of which may bespecific to a particular operating system or platform.

The term “module” used in this disclosure may refer to a certain unitthat includes one of hardware, software and firmware or any combinationthereof. The module may be interchangeably used with unit, logic,logical block, component, or circuit, for example. The module may be theminimum unit, or part thereof, which performs one or more particularfunctions. The module may be formed mechanically or electronically. Forexample, the module disclosed herein may include at least one ofapplication-specific integrated circuit (ASIC) chip, field-programmablegate arrays (FPGAs), and programmable-logic device, which have beenknown or are to be developed.

Further, the architecture 2000 depicts an aggregation of VPA basedmechanisms and ML/NLP based mechanism in accordance with an embodimentof the present subject matter. A user-interface defined as input andinteraction 2001 refers to overall input. It can include one or more ofthe following—a touch screen, a microphone, a camera, or the like. Afirst hardware module 2002 depicts specialized hardware for ML/NLP basedmechanisms. In an example, the first hardware module 2002 comprises oneor more of neural processors, FPGA, digital signal processor (DSP), GPU,or the like.

A second hardware module 2012 depicts specialized hardware for executingthe VPA device-related audio and video simulations. ML/NLP basedframeworks and APIs 2004 correspond to the hardware interface layer forexecuting the ML/NLP logic 2006 based on the underlying hardware. In anexample, the frameworks may be one or more or the following—Tensorflow,Café, NLTK, GenSim, ARM Compute, or the like. VPA simulation 2016frameworks and APIs 2014 may include one or more of— VPA Core, VPA Kit,Unity, Unreal, or the like.

A database 2008 depicts a pre-trained voice feature database. Thedatabase 2008 may be remotely accessible through cloud. In otherexample, the database 2008 may partly reside on cloud and partlyon-device based on usage statistics.

Another database 2018 refers the speaker enrollment DB or the voicefeature DB that will be used to authenticate and respond to the user.The database 2018 may be remotely accessible through cloud. In otherexample, the database 2018 may partly reside on the cloud and partlyon-device based on usage statistics.

A rendering module 2005 is provided for rendering audio output andtrigger further utility operations as a result of user authentication.The rendering module 2005 may be manifested as a display, a touchscreen, a monitor, a speaker, a projection screen, or the like.

A general-purpose hardware and driver module 2003 corresponds to thecomputing device 202 as referred in FIG. 2B and instantiates drivers forthe general purpose hardware units as well as the application-specificunits (2002, 2012).

In an example, the NLP/ML mechanism and VPA simulations 2016 underlyingthe present architecture 2000 may be remotely accessible andcloud-based, thereby being remotely accessible through a networkconnection. A computing device, such as a VPA device may be configuredfor remotely accessing the NLP/ML modules and simulation modules maycomprise skeleton elements, such as a microphone, a camera ascreen/monitor, a speaker, or the like.

While specific language has been used to describe the disclosure, anylimitations arising on account of the same are not intended. As would beapparent to a person in the art, various working modifications may bemade to the method in order to implement the inventive concept as taughtherein. The drawings and the forgoing description give examples ofembodiments. Those skilled in the art will appreciate that one or moreof the described elements may well be combined into a single functionalelement. Alternatively, certain elements may be split into multiplefunctional elements. Elements from one embodiment may be added toanother embodiment. For example, orders of processes described hereinmay be changed and are not limited to the manner described herein.

Moreover, the actions of any flow diagram need not be implemented in theorder shown, nor do all of the acts necessarily need to be performed. Inaddition, those acts that are not dependent on other acts may beperformed in parallel with the other acts. The scope of embodiments isby no means limited by these specific examples. Numerous variations,whether explicitly given in the specification or not, such asdifferences in structure, dimension, and use of material, are possible.The scope of embodiments is at least as broad as given by the followingclaims.

While the disclosure has been shown and described with reference tovarious embodiments thereof, it will be understood by those skilled inthe art that various changes in form and details may be made thereinwithout departing from the spirit and scope of the disclosure as definedby the appended claims and their equivalents.

What is claimed is:
 1. An apparatus for generating one or more emoticonsfor one or more users with respect to one or more fictional characters,the apparatus comprising: a plurality of discriminators configured to:receive a first image generated by a multiple localized discriminator(MLD) generative adversarial network (GAN) based on a set of featuresfrom multiple sets of features associated with the one or more fictionalcharacters, resulting in generation of an output value associated witheach of the plurality of discriminators, and determine a weightassociated with each of the plurality of discriminators based on adistance between each discriminator and the set of features; at leastone processor configured to generate, using a pre-trained info graph, animage info-graph associated with the first image generated by the MLDGAN upon receiving the first image; calculate a relevance associatedwith each of the plurality of discriminators based on the imageinfo-graph, the set of features and the distance; and the MLD GANconfigured to: generate a plurality of images representing a pluralityof emoticons associated with the one or more fictional characters basedon each of the multiple sets of features, and generate the one or moreemoticons by styling one or more user images with respect to one or moreimages selected from the plurality of images, and one or more userinputs.
 2. The apparatus of claim 1, wherein the one or more user inputscomprise a plurality of facial features, a facial tone, a hair color, abody built, a dressing style, and one or more accessories worn by theone or more users.
 3. The apparatus of claim 1, wherein the at least oneprocessor is further configured to: calculate a confidence scoreassociated with the plurality of discriminators based on a plurality ofparameters associated with each of the plurality of discriminators, andselect remaining sets of features after generation of the first imagebased on the confidence score associated with the plurality ofdiscriminators.
 4. The apparatus of claim 3, wherein a set of featurescorresponding to a discriminator with a high relevance among theplurality of discriminators is prioritized for selection.
 5. Theapparatus of claim 3, wherein the plurality of parameters comprise aweight, the output value, and the relevance associated with each of theplurality of discriminators.
 6. The apparatus of claim 1, wherein theone or more images is selected from the plurality of images based on theoutput value of a discriminator associated with the one or more imagesfrom the plurality of discriminators.
 7. The apparatus of claim 1,wherein the styling of the one or more user images comprises:super-imposing the one or more images generated from the set of featuresassociated with the one or more fictional characters onto the one ormore user images; and applying the one or more user inputs therebygenerating the one or more emoticons.
 8. The apparatus of claim 1,wherein the one or more fictional characters is based on one or more ofa story, a conversation, a textual input, and a voice input.
 9. Theapparatus of claim 1 wherein the at least one processor is furtherconfigured to: generate a fictional character info-graph associated withthe one or more fictional characters based on a plurality of featuresassociated with the one or more fictional characters, generate a userinfo-graph associated with the one or more users based on a plurality ofattributes associated with the one or more users, and map the one ormore fictional characters to the one or more users based on theplurality of features and the plurality of attributes.
 10. The apparatusof claim 9, wherein the fictional character info-graph is generated by:analyzing, by a natural language processing(NLP) processor, one or moreconversations and one or more dialogue exchanges between the one or morefictional characters with respect to an event node of a plot graphassociated with the one or more fictional characters; and determining,by the NLP processor, the plurality of features upon analyzing the oneor more conversations and one or more dialogue exchanges, and whereinthe plurality of features comprises: an interaction profile from one ormore event nodes shared by the one or more fictional characters, anemotional pallet associated with the one or more fictional charactersdescribing a level of expression of one or more emotions experienced bythe one or more fictional characters, and one or more physical traitsbased on a textual description from the plot graph and one or morepersonality traits from the interaction profile and the emotion palletfor the one or more fictional characters.
 11. The apparatus of claim 9,wherein the user info-graph is generated by: analyzing, by the NLPprocessor, one or more conversations and one or more dialogue exchangesbetween the one or more users with respect to an event node of a plotgraph associated with the one or more users and one or more social mediaactivities of the one or more users; and determining, by the NLPprocessor, the plurality of attributes upon analyzing the one or moreconversations and one or more dialogue exchanges, and wherein theplurality of attributes comprise: an interaction profile from one ormore event nodes shared by the one or more users, an emotional palletassociated with the one or more users describing a level of expressionof one or more emotions experienced by the one or more users, and one ormore physical traits based on a textual description from the plot graphand one or more personality traits from the interaction profile and theemotional pallet for the user.
 12. The apparatus of claim 11, furthercomprising: parsing, by an artificial intelligence(AI) processor, one ormore of an audio input and a visual input to a textual form; andsharing, by the AI processor, the textual form with the, by the NLPprocessor, for determining the interaction profile, the emotionalpallet, and the one or more physical traits and the one or morepersonality traits.
 13. The apparatus of claim 12, wherein the parsingby the AI processor comprises processing of an image, an audio, a video,and a multimedia content received in the form of the one or more of theaudio input, and the visual input.
 14. The apparatus of claim 10 whereinthe plot graph is a representation of a space of a plurality of possiblestories associated with a scenario.
 15. The apparatus of claim 9,wherein the mapping of the one or more fictional characters to the oneor more users comprises: calculating a distance between the plurality offeatures and the plurality of attributes; and creating a mappingassociated with the one or more users mapped corresponding to the one ormore fictional character based on the distance, and wherein the one ormore users is mapped to the one or more fictional characters.
 16. Theapparatus of claim 9, wherein the at least one processor is furtherconfigured to select, from a content database, the plurality of featuresassociated with the one or more fictional characters and the pluralityof attributes associated with the one or more users.
 17. The apparatusof claim 1, wherein the at least one processor is further configured toreceive the fictional character info-graph comprising the plurality offeatures associated with the one or more fictional characters; selectone or more features from the plurality of features uniquely definingthe one or more fictional characters and transmitting the set offeatures; and generate the plurality of images upon receiving themultiple sets of features generated from the one or more featuresassociated with the one or more fictional characters and transmittingthe plurality of images to the plurality of discriminators.
 18. Theapparatus of claim 17, wherein the selecting of the one or more featurescomprises: determining a character closeness ratio representing acloseness between the one or more fictional characters based on adistance between the one or more fictional characters.
 19. The apparatusof claim 17, wherein the at least one processor is further configured toreceive a user input as one of an audio input and a textual input; andselect the one or more features from the fictional character info-graphbased on the user input, wherein the one or more features uniquelydefine the one or more fictional characters.
 20. A method of generatingone or more emoticons for one or more users with respect to one or morefictional characters, the method comprising: receiving, by a pluralityof discriminators, a first image generated by a multiple localizeddiscriminator (MLD) generative adversarial network (GAN) based on a setof features from multiple sets of features associated with the one ormore fictional characters, resulting in generation of an output valueassociated with each of the plurality of discriminators; determining, bythe plurality of discriminators, a weight associated with each of theplurality of discriminators based on a distance between eachdiscriminator and the set of features; generating, by at least oneprocessor, an image info-graph associated with the first image generatedby the MLD GAN upon receiving the first image; calculating a relevanceassociated with each of the plurality of discriminators based on theimage info-graph, the set of features and the distance; generating, bythe MLD GAN, a plurality of images representing a plurality of emoticonsassociated with the one or more fictional characters based on each ofthe multiple sets of features; and generating, by the MLD GAN, the oneor more emoticons by styling one or more user images with respect to oneor more images selected from the plurality of images, and one or moreuser inputs.